基于多变量加权一阶局域混沌预测模型优化及应用

doi:10.3969/j.issn.1000-1158.2018.01.17

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Abstract In view of the influence of multi-variable on the chaotic prediction in practical application, a method for phase space reconstruction of multivariate time series is proposed, and a weighted one-rank local chaos forecasting model on multi-variable was established. The equal probability-based maximum joint entropy and the minimum Shannon entropy are introduced to get the delay time and the embedding dimension respectively, realizing the sub-sequence reconstruction to the chaotic prediction model. The nearest neighbor point method is used to determine the neighborhood of the prediction center to avoid false neighbors, and the correlation analysis is used to determine the observed variables. The model was applied to short-term load forecasting, and the temperature time series was introduced as another observation variable by the analysis of the impact of temperature and other factors related to electric load. The experimental results showed that the prediction accuracy was improved compared with the single variable forecasting method.

Key words： metrology short-term load forecasting weighted one-rank local region method chaos prediction model optimization equal probability symbolization maximum joint entropy Shannon entropy correlation analysismultivariable prediction

Received: 30 December 2015 Published: 29 December 2017

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TB971

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	Articles by authors
	ZHANG Shu-qing
	LIU Zi-yue
	HE Hong-yun
	REN Shuang
	ZHANG Li-guo
	JIANG Wan-lu

Cite this article:

ZHANG Shu-qing,LIU Zi-yue,HE Hong-yun, et al. Optimization and Application of Weighted One-rank Local Chaos Prediction Model Based on Multi-variable[J]. Acta Metrologica Sinica, 2018, 39(1): 77-82.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2018.01.17 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2018/V39/I1/77

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